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ABSTRACT Innovative technology helps students foster creative thinking and problem‐solving abilities by augmenting human sensing and enriching input and output information. New technology can incorporate haptic sensing features—a sensing modality for user operations. Learning with haptic sensing features promises new ways to master cognitive and motor skills and higher‐order cognitive reasoning tasks (e.g., decision‐making and problem‐solving). This study conceptualizes haptic technology within the human‐technology interaction (HTI) framework. It aims to investigate the components of haptic systems to define their impact on learning and facilitate understanding of haptic technology, including application development to ease entry barriers for educators. The research builds a haptic HTI framework based on a systematic literature review on haptic applications in engineering learning over the last two decades. The review utilizes the SALSA methodology to analyze relevant studies comprehensively. The framework outcome is a haptic HTI taxonomy to build visual representations of the explicit connection between the taxonomy components and practical educational applications (by means of heatmaps). The approach led to a robust conceptualization of HTI into a taxonomy—a structured framework encompassing categories for interaction modalities, immersive technologies, and learning methodologies in engineering education. The model assists in understanding how haptic feedback can be utilized in learning with technology experiences. Applying haptic technology in engineering education includes mastering fundamental science concepts and creating customized haptic prototypes for engineering processes. A growing trend focuses on wearable haptics, such as gloves and vests, which involve kinesthetic movement, fine motor skills, and spatial awareness—all fostering spatial and temporal cognitive abilities (the ability to effectively manage and comprehend significant amounts ofspatial(how design components or resources are related to one another in the 3D space) andtemporal(the logic in a process, such as the order, sequences, and hierarchies of the resources information). The haptic human‐technology interaction (H‐HTI) framework guides future research in developing cognitive reasoning through H‐HTI, unlocking new frontiers in engineering education. 

Integrating complex spatio-temporal cognitive tasks such as in-situ planning and trade coordination of job site activities is a continuous challenge to learners in Construction Engineering (CE) courses. Spatial information in this context addresses how physical resources are related to one another at a job site, whereas temporal information defines work sequences and hierarchies that transform physical resources. This paper discusses the impacts of using an innovative learning environment for supporting spatio-temporal cognition in CE education using aerial visualizations from Unmanned Aerial Vehicles (UAVs). Learners experience a unique, ‘birds-eye view’ of the spatio-temporal dynamics of a job site. The effects were on improved abilities to apply, analyze, and synthesize any form of design representation to situations and physical contexts. Our findings demonstrate that participants in the intervention group outperformed the control group on measures of learning and motivation, which underscores the potential of UAVs as an educational technology system in CE education. 

Project control operations in construction are mostly executed via direct observations and the manual monitoring of progress and performance of construction tasks on the job site. Project engineers move physically within job-site areas to ensure activities are executed as planned. Such physical displacements are error-prone and ineffective in cost and time, particularly in larger construction zones. It is critical to explore new methods and technologies to effectively assist performance control operations by rapidly capturing data from materials and equipment on the job site. Motivated by the ubiquitous use of unmanned aerial vehicles (UAVs) in construction projects and the maturity of computer-vision-based machine-learning (ML) techniques, this research investigates the challenges of object detection—the process of predicting classes of objects (specified construction materials and equipment)—in real time. The study addresses the challenges of data collection and predictions for remote monitoring in project control activities. It uses these two proven and robust technologies by exploring factors that impact the use of UAV aerial images to design and implement object detectors through an analytical conceptualization and a showcase demonstration. The approach sheds light on the applications of deep-learning techniques to access and rapidly identify and classify resources in real-time. It paves the way to shift from costly and time-consuming job-site walkthroughs that are coupled with manual data processing and input to more automated, streamlined operations. The research found that the critical factor to develop object detectors with acceptable levels of accuracy is collecting aerial images with for adequate scales with high frequencies from different positions of the same construction areas.